Joint Antenna Selection Research Articles

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

Joint Antenna Selection and Power Allocation Method Based on Quantum Energy Valley Optimization Algorithm for Massive MIMO IoT Systems

Joint antenna selection and resource allocation for mm‐wave directional D2D communications using distributed deep reinforcement learning

AbstractIn this paper, with the promising assumption of using adaptive directional microstrip antenna on user equipment, the problem of joint antenna selection, spectrum assignment, and transmit power allocation for mm‐wave device‐to‐device communications underlying cellular networks is tackled, with the goal of enhancing system throughput and energy efficiency. To address the complexity of this problem, a method based on multi‐agent distributed deep reinforcement learning in which an autonomous intelligent agent is deployed for each user equipment is proposed. The performance evaluation demonstrates its superiority over existing strategies, resulting in improved system performance, reduced outage probability, and enhanced energy efficiency.

Joint antenna selection and power allocation in massive MIMO systems with cell division technique for MRT and ZF precoding schemes

Joint antenna selection and power allocation in massive MIMO systems with cell division technique for MRT and ZF precoding schemes

Joint Antenna Selection and Beamforming for Area Surveillance With Spatially Distributed Array Radar

Joint Antenna Selection and Beamforming for Area Surveillance With Spatially Distributed Array Radar

Low-Complexity Joint Antenna Selection and Robust Multi-Group Multicast Beamforming for Massive MIMO

Low-Complexity Joint Antenna Selection and Robust Multi-Group Multicast Beamforming for Massive MIMO

Joint Antenna Selection and Multicast Precoding in Spatial Modulation Systems

Joint Antenna Selection and Multicast Precoding in Spatial Modulation Systems

Secrecy energy efficiency optimization for multiuser massive MIMO wiretap systems with transmit antenna selection

Massive multiple-input-multiple-output (Massive MIMO) significantly improves the capacity of wireless communication systems. However, large-scale antennas bring high hardware costs, and security is a vital issue in Massive MIMO networks. To deal with the above problems, antenna selection (AS) and artificial noise (AN) are introduced to reduce energy consumption and improve system security performance, respectively. In this paper, we optimize secrecy energy efficiency (SEE) in a downlink multi-user multi-antenna scenario, where a multi-antenna eavesdropper attempts to eavesdrop the information from the base station (BS) to the multi-antenna legitimate receivers. An optimization problem is formulated to maximize the SEE by jointly optimizing the transmit beamforming vectors, the artificial noise vector and the antenna selection matrix at the BS. The formulated problem is a nonconvex mixed integer fractional programming problem. To solve the problem, a successive convex approximation (SCA)-based joint antenna selection and artificial noise (JASAN) algorithm is proposed. After a series of relaxation and equivalent transformations, the nonconvex problem is approximated to a convex problem, and the solution is obtained after several iterations. Simulation results show that the proposed algorithm has good convergence behavior, and the joint optimization of antenna selection and artificial noise can effectively improve the SEE while ensuring the achievable secrecy rate.

A POMDP-Based Approach to Joint Antenna Selection and User Scheduling for Multi-User Massive MIMO Communication

We devise a partially observable Markov decision process (POMDP) based joint antenna selection and user scheduling (JASUS) policy for a massive MIMO base station, equipped with only a small number of RF chains, that serves a large number of users. The users are served at different time slots within a frame. Relying on partial CSI obtained from training between the selected antennas and the users, at the beginning of each frame, the BS assigns each user to a time slot in the frame and selects a subset of antennas to serve the users scheduled in each time slot. Assuming that the channels evolve according to a Markov process and relying on zero-forcing beamforming, we formulate our JASUS problem using a POMDP framework to devise a real-time decision-making policy that maximizes the expected long-term sum-rate. We rigorously prove that for positively correlated two-state channel models, the myopic policy provides the optimal solution to our POMDP-based JASUS problem for any number of RF chains and for any number of users. Based on this, we model the Rayleigh fading channels as first-order Gauss-Markov processes and devise a low-complexity myopic policy-based JASUS algorithm for massive MU-MIMO systems that only relies on partial CSI.

Deep learning based low complexity joint antenna selection scheme for MIMO vehicular adhoc networks

In this paper, we propose deep learning based transmit and receive antenna selection scheme for multiple-input-multiple-output vehicular adhoc networks (MIMO-VANETs). The dynamic nature of VANETs instills the need for regular updates in antenna selections, moreover massive computations involved for antenna selection due to changes in channel statistics results in large delay in message communication. The delay in antenna selection in case of conventional approaches can be reduced substantially by automating the redundant computations using deep learning approach. In this work norm-based low complexity joint transmit and receive antenna selection along with its implementation through convolution neural network has been proposed. The proposed supervised deep learning approach to select a group of antennas to enhance capacity and minimize delay has shown encouraging results in MIMO VANET scenario as compared to the conventional antenna selection scheme.

Joint antenna selection and waveform design for coexistence of MIMO radar and communications

As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the ell _{2,1}-norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas.

Diversity achieving full‐duplex DF relaying with joint relay‐antenna selection under Nakagami‐m fading environment

SummaryOne of the main imperfections degrading the performance of full‐duplex (FD) relaying systems is the outage floor. In this work, a power scaling method is proposed, which overcomes this effect even when there does not exist a direct channel between source and destination nodes. The system is composed of decode‐and‐forward (DF) FD relays over the Nakagami‐m fading environment. To promote system performance, joint antenna and relay selection methods are employed in the FD relaying systems. Each FD relay is equipped with multiple antennas for receiving and the other for transmitting the information. The transmitting and receiving antennas are chosen based on the instantaneous channel variations, and one relay is selected to improve the signal‐to‐interference and noise ratio (SINR) of the FD relaying system. The performance of the proposed design is investigated. Moreover, the closed‐form equations of the ergodic capacity and outage probability are attained. The analytical results are confirmed by different simulations. Results indicate that the proposed design achieves an additional spatial diversity gain because of using the antenna selection at the relay nodes. Moreover, by power scaling (PS) method, the system performance is effectively improved compared to the conventional FD relaying structures.

Energy efficient power allocation with joint antenna and user selection in massive MIMO systems

The problem of maximizing energy efficiency (EE) is considered in this paper. The system in question is a massive multiple-input multiple-output (MIMO) system that transmits information simultaneously in the uplink and downlink. To maximize EE, an optimal antenna selection strategy is used to send information. Furthermore, we propose an optimal user selection method and then obtain their optimal allocated power. The optimization problem of the system has two constraints, including power limitations in the uplink and downlink. Due to the non-convexity and being constrained of the optimization problem, in order to obtain the optimal power of users, it must first be converted to a convex problem form and then turned into an unconstrained problem using the Lagrange dual function method. The bisection algorithm is utilized to select the optimal set of active antennas for sending information. The cross-entropy algorithm with the proposed algorithm is used simultaneously to select the set of users and determine their optimal power values. The results of computer simulations show that the proposed selection schemes of antennas and users significantly improve the system performance.

Full-Duplex Buffer-Aided MIMO Relaying Networks: Joint Antenna Selection and Rate Allocation Based on Buffer Status

The in-band full-duplex buffer-aided relaying is a spectral-efficient technology for extending the range of reliable communication. In this context, we introduce a novel joint buffer-aware rate allocation and antenna selection algorithm to schedule the network transmissions. The closed-form expression for the average throughput of the network is derived under Nakagami-m fading environment. Through Monte-Carlo simulations, we demonstrate that our proposed buffer-aware approach significantly outperforms the conventional schemes with prefixed antenna allocation considering two benchmarks operating based on the receive-antenna-selection (RAS)/transmit antenna selection (TAS) and maximum-ratio-combining (MRC)/TAS. The overlapping results in the presented simulation examples corroborates our theoretical analysis.

Optimal Energy-Efficient Antenna Selection and Power Adaptation for Interference-Outage Constrained Underlay Spectrum Sharing

Underlay spectrum sharing addresses spectrum scarcity but imposes constraints on the interference the secondary system causes to the primary receiver. For a secondary transmitter that is subject to the stochastic and general interference-outage constraint and the peak transmit power constraint, we present a novel joint antenna selection and power adaptation rule. It addresses the twin goals of improving the spectral efficiency and reducing the power consumed of an underlay secondary system with low hardware complexity and cost. We prove that the rule maximizes the energy-efficiency (EE) of the secondary system. Its form differs from all other rules considered in the literature. We then present an insightful geometrical characterization of the optimal power of the selected antenna in terms of the channel gains within the secondary system and between the secondary and primary systems. Our approach leads to several special cases, which are themselves novel, and novel analytical insights about the performance and structure of the optimal rule. We also present an iterative subgradient-based algorithm and a simpler non-iterative bound-based algorithm to compute the rule’s parameters. The rule achieves a markedly higher EE compared to conventional approaches, and serves as a new fundamental benchmark for antenna selection in underlay spectrum sharing.

Reconfigurable Intelligent Surface Assisted Massive MIMO With Antenna Selection

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate for the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Moreover, the proposed algorithms are feasible to any finite number of antennas, and thus can be applicable in both ordinary MIMO and massive MIMO settings. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS-assisted massive MIMO systems with antenna selection.

Deep Unsupervised Learning for Joint Antenna Selection and Hybrid Beamforming

In this paper, we propose a novel deep unsupervised learning-based approach that jointly optimizes antenna selection and hybrid beamforming to improve the hardware and spectral efficiencies of massive multiple-input-multiple-output (MIMO) downlink systems. By employing ResNet to extract features from the channel matrices, two neural networks, i.e., the antenna selection network (ASNet) and the hybrid beamforming network (BFNet), are respectively proposed for dynamic antenna selection and hybrid beamformer design. Furthermore, a deep probabilistic subsampling trick and a specially designed quantization function are respectively developed for ASNet and BFNet to preserve the differentiability while embedding discrete constraints into the network structures. With the aid of a flexibly designed loss function, ASNet and BFNet are jointly trained in a phased unsupervised way, which avoids the prohibitive computational cost of acquiring training labels in supervised learning. Simulation results demonstrate the advantage of the proposed approach over conventional optimization-based algorithms in terms of both the achieved rate and the computational complexity.

An Optimal Selection Algorithm Based on Q-Learning For Dual Media Communication System With Full-Duplex Relays

Full-duplex communication technology can make full use of spectrum resources through simultaneous co-frequency transmission, but the interference of the signal sent by the device itself degrades performance. The paper proposes a joint antenna and relay selection algorithm based on Q-learning, which selects the best relay with the best transmit-receive configuration for the communication between the source and the target. Specifically, by learning the dual-medium cooperative communication environment to obtain feedback in terms of interruption probability and channel capacity, so as to determine the best relay and antenna. Finally, the paper verifies the effectiveness of the algorithm by simulation.

Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.

Machine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> RF chains and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M &lt; N$ </tex-math></inline-formula> . Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD algorithm overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tests all valid antenna subsets. Although approaching (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between key system parameters and the selected antennas. The proposed L-ASPD algorithm is robust against the number of users and their locations, the transmit power of the BS, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD algorithm significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves a better effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD algorithm can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance.

Performance analysis of joint transmit antenna selection and user scheduling for massive MIMO systems

To alleviate the problem of hardware complexity, energy wastage and increased cost of deployment in massive multiple input multiple output (MIMO) system, joint antenna selection and user scheduling...